Search Results for author: Zhuzhu Wang

Found 5 papers, 1 papers with code

Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear

1 code implementation1 Jun 2023 Ruohan Gao, Hao Li, Gokul Dharan, Zhuzhu Wang, Chengshu Li, Fei Xia, Silvio Savarese, Li Fei-Fei, Jiajun Wu

We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear.

Multi-Task Learning Visual Navigation

NLP-based Cross-Layer 5G Vulnerabilities Detection via Fuzzing Generated Run-Time Profiling

no code implementations14 May 2023 Zhuzhu Wang, Ying Wang

The effectiveness and efficiency of 5G software stack vulnerability and unintended behavior detection are essential for 5G assurance, especially for its applications in critical infrastructures.

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

no code implementations16 Feb 2022 Shiliang Zhang, Xuehui Ma, Hui Cao, Tengyuan Zhao, Yajie Yu, Zhuzhu Wang

To this end, we design a lightweight approach dedicating to privatizing image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable.

Attribute

Cloud-based Federated Boosting for Mobile Crowdsensing

no code implementations9 May 2020 Zhuzhu Wang, Yilong Yang, Yang Liu, Ximeng Liu, Brij B. Gupta, Jianfeng Ma

In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing.

Federated Learning General Classification +3

Revocable Federated Learning: A Benchmark of Federated Forest

no code implementations8 Nov 2019 Yang Liu, Zhuo Ma, Ximeng Liu, Zhuzhu Wang, Siqi Ma, Ken Ren

A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the local data.

Federated Learning

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